




"""simple docstring"""




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase				:     List[Any]					=				[
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

lowerCAmelCase				:     List[Any]					=				[
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

lowerCAmelCase				:     Optional[int]					=				[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
					# loop over downblocks/upblocks

					for j in range(2):
										# loop over resnets/attentions for downblocks
										lowerCAmelCase				:     List[Any]					=				F"""down_blocks.{i}.resnets.{j}."""
										lowerCAmelCase				:     Union[str, Any]					=				F"""input_blocks.{3*i + j + 1}.0."""
										unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

										if i < 3:
															# no attention layers in down_blocks.3
															lowerCAmelCase				:     Optional[int]					=				F"""down_blocks.{i}.attentions.{j}."""
															lowerCAmelCase				:     List[Any]					=				F"""input_blocks.{3*i + j + 1}.1."""
															unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

					for j in range(3):
										# loop over resnets/attentions for upblocks
										lowerCAmelCase				:     Optional[Any]					=				F"""up_blocks.{i}.resnets.{j}."""
										lowerCAmelCase				:     Union[str, Any]					=				F"""output_blocks.{3*i + j}.0."""
										unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

										if i > 0:
															# no attention layers in up_blocks.0
															lowerCAmelCase				:     int					=				F"""up_blocks.{i}.attentions.{j}."""
															lowerCAmelCase				:     Any					=				F"""output_blocks.{3*i + j}.1."""
															unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

					if i < 3:
										# no downsample in down_blocks.3
										lowerCAmelCase				:     Union[str, Any]					=				F"""down_blocks.{i}.downsamplers.0.conv."""
										lowerCAmelCase				:     Tuple					=				F"""input_blocks.{3*(i+1)}.0.op."""
										unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

										# no upsample in up_blocks.3
										lowerCAmelCase				:     Any					=				F"""up_blocks.{i}.upsamplers.0."""
										lowerCAmelCase				:     Any					=				F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
										unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase				:     List[Any]					=				"""mid_block.attentions.0."""
lowerCAmelCase				:     Tuple					=				"""middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
					lowerCAmelCase				:     Tuple					=				F"""mid_block.resnets.{j}."""
					lowerCAmelCase				:     str					=				F"""middle_block.{2*j}."""
					unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def        a__					(							snake_case__							)		->   Dict:
			# buyer beware: this is a *brittle* function,
			# and correct output requires that all of these pieces interact in
			# the exact order in which I have arranged them.
			lowerCamelCase      =   {k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						lowerCamelCase      =   sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												lowerCamelCase      =   v.replace(snake_case__			,					snake_case__							)
									lowerCamelCase      =   v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									lowerCamelCase      =   v.replace(snake_case__			,					snake_case__							)
						lowerCamelCase      =   v
			lowerCamelCase      =   {v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase				:     Any					=				[
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
					# down_blocks have two resnets
					for j in range(2):
										lowerCAmelCase				:     List[str]					=				F"""encoder.down_blocks.{i}.resnets.{j}."""
										lowerCAmelCase				:     List[str]					=				F"""encoder.down.{i}.block.{j}."""
										vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

					if i < 3:
										lowerCAmelCase				:     List[Any]					=				F"""down_blocks.{i}.downsamplers.0."""
										lowerCAmelCase				:     Any					=				F"""down.{i}.downsample."""
										vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

										lowerCAmelCase				:     Any					=				F"""up_blocks.{i}.upsamplers.0."""
										lowerCAmelCase				:     Dict					=				F"""up.{3-i}.upsample."""
										vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

					# up_blocks have three resnets
					# also, up blocks in hf are numbered in reverse from sd
					for j in range(3):
										lowerCAmelCase				:     int					=				F"""decoder.up_blocks.{i}.resnets.{j}."""
										lowerCAmelCase				:     str					=				F"""decoder.up.{3-i}.block.{j}."""
										vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
					lowerCAmelCase				:     str					=				F"""mid_block.resnets.{i}."""
					lowerCAmelCase				:     List[str]					=				F"""mid.block_{i+1}."""
					vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase				:     Union[str, Any]					=				[
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]



def        a__					(							snake_case__							)		->   Tuple:
			# convert HF linear weights to SD conv2d weights
			return w.reshape(*w.shape			,					1			,					1							)



def        a__					(							snake_case__							)		->   str:
			lowerCamelCase      =   {k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowerCamelCase      =   v.replace(snake_case__			,					snake_case__							)
						lowerCamelCase      =   v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowerCamelCase      =   v.replace(snake_case__			,					snake_case__							)
									lowerCamelCase      =   v
			lowerCamelCase      =   {v: vae_state_dict[k] for k, v in mapping.items()}
			lowerCamelCase      =   ["""q""", """k""", """v""", """proj_out"""]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if F'mid.attn_1.{weight_name}.weight' in k:
												print(F'Reshaping {k} for SD format'							)
												lowerCamelCase      =   reshape_weight_for_sd(snake_case__							)
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase				:     Tuple					=				[
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
lowerCAmelCase				:     str					=				{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase				:     Union[str, Any]					=				re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase				:     Optional[int]					=				{"""q""": 0, """k""": 1, """v""": 2}



def        a__					(							snake_case__							)		->   Any:
			lowerCamelCase      =   {}
			lowerCamelCase      =   {}
			lowerCamelCase      =   {}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(""".self_attn.q_proj.weight"""							)
						    or k.endswith(""".self_attn.k_proj.weight"""							)
						    or k.endswith(""".self_attn.v_proj.weight"""							)
						):
									lowerCamelCase      =   k[: -len(""".q_proj.weight"""							)]
									lowerCamelCase      =   k[-len("""q_proj.weight"""							)]
									if k_pre not in capture_qkv_weight:
												lowerCamelCase      =   [None, None, None]
									lowerCamelCase      =   v
									continue

						if (
						    k.endswith(""".self_attn.q_proj.bias"""							)
						    or k.endswith(""".self_attn.k_proj.bias"""							)
						    or k.endswith(""".self_attn.v_proj.bias"""							)
						):
									lowerCamelCase      =   k[: -len(""".q_proj.bias"""							)]
									lowerCamelCase      =   k[-len("""q_proj.bias"""							)]
									if k_pre not in capture_qkv_bias:
												lowerCamelCase      =   [None, None, None]
									lowerCamelCase      =   v
									continue

						lowerCamelCase      =   textenc_pattern.sub(lambda snake_case__							: protected[re.escape(m.group(0							)							)]			,					snake_case__							)
						lowerCamelCase      =   v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""							)
						lowerCamelCase      =   textenc_pattern.sub(lambda snake_case__							: protected[re.escape(m.group(0							)							)]			,					snake_case__							)
						lowerCamelCase      =   torch.cat(snake_case__							)

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""							)
						lowerCamelCase      =   textenc_pattern.sub(lambda snake_case__							: protected[re.escape(m.group(0							)							)]			,					snake_case__							)
						lowerCamelCase      =   torch.cat(snake_case__							)

			return new_state_dict



def        a__					(							snake_case__							)		->   Optional[int]:
			return text_enc_dict


if __name__ == "__main__":
					lowerCAmelCase				:     Optional[Any]					=				argparse.ArgumentParser()

					parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
					parser.add_argument(
					    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
					)

					lowerCAmelCase				:     int					=				parser.parse_args()

					assert args.model_path is not None, "Must provide a model path!"

					assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

					# Path for safetensors
					lowerCAmelCase				:     List[Any]					=				osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
					lowerCAmelCase				:     Tuple					=				osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
					lowerCAmelCase				:     int					=				osp.join(args.model_path, """text_encoder""", """model.safetensors""")

					# Load models from safetensors if it exists, if it doesn't pytorch
					if osp.exists(unet_path):
										lowerCAmelCase				:     Optional[int]					=				load_file(unet_path, device="""cpu""")
					else:
										lowerCAmelCase				:     Tuple					=				osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
										lowerCAmelCase				:     int					=				torch.load(unet_path, map_location="""cpu""")

					if osp.exists(vae_path):
										lowerCAmelCase				:     Optional[Any]					=				load_file(vae_path, device="""cpu""")
					else:
										lowerCAmelCase				:     Optional[int]					=				osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
										lowerCAmelCase				:     List[str]					=				torch.load(vae_path, map_location="""cpu""")

					if osp.exists(text_enc_path):
										lowerCAmelCase				:     Dict					=				load_file(text_enc_path, device="""cpu""")
					else:
										lowerCAmelCase				:     List[Any]					=				osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
										lowerCAmelCase				:     str					=				torch.load(text_enc_path, map_location="""cpu""")

					# Convert the UNet model
					lowerCAmelCase				:     Optional[Any]					=				convert_unet_state_dict(unet_state_dict)
					lowerCAmelCase				:     Any					=				{"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

					# Convert the VAE model
					lowerCAmelCase				:     Tuple					=				convert_vae_state_dict(vae_state_dict)
					lowerCAmelCase				:     Optional[Any]					=				{"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

					# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
					lowerCAmelCase				:     int					=				"""text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

					if is_vaa_model:
										# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
										lowerCAmelCase				:     Union[str, Any]					=				{"""transformer.""" + k: v for k, v in text_enc_dict.items()}
										lowerCAmelCase				:     int					=				convert_text_enc_state_dict_vaa(text_enc_dict)
										lowerCAmelCase				:     str					=				{"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
					else:
										lowerCAmelCase				:     Dict					=				convert_text_enc_state_dict(text_enc_dict)
										lowerCAmelCase				:     Union[str, Any]					=				{"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

					# Put together new checkpoint
					lowerCAmelCase				:     Optional[Any]					=				{**unet_state_dict, **vae_state_dict, **text_enc_dict}
					if args.half:
										lowerCAmelCase				:     str					=				{k: v.half() for k, v in state_dict.items()}

					if args.use_safetensors:
										save_file(state_dict, args.checkpoint_path)
					else:
										lowerCAmelCase				:     Tuple					=				{"""state_dict""": state_dict}
										torch.save(state_dict, args.checkpoint_path)









"""simple docstring"""




def        a__					(							snake_case__ = 50_00_00_00							)		->   int:
			lowerCamelCase      =   set()
			lowerCamelCase      =   int((limit - 24) ** (1 / 2)							)

			lowerCamelCase      =   set(range(3			,					prime_square_limit + 1			,					2							)							)
			primes.add(2							)
			for p in range(3			,					prime_square_limit + 1			,					2							):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p			,					prime_square_limit + 1			,					snake_case__							)							)							)

			for primea in primes:
						lowerCamelCase      =   primea * primea
						for primea in primes:
									lowerCamelCase      =   primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												lowerCamelCase      =   primea * primea * primea * primea
												lowerCamelCase      =   square + cube + tetr
												if total >= limit:
															break
												ret.add(snake_case__							)

			return len(snake_case__							)


if __name__ == "__main__":
					print(F"""{solution() = }""")



